Distilled

Welcome to this week's Distilled!

Your AI Research Assistant Just Got a Team of Specialists TL;DR — Paper Circle uses multiple AI agents working together to find relevant research papers, build knowledge maps showing how ideas connect, and generate detailed reviews—all while showing you e…

LLMs That Learn While They Think TL;DR — Researchers found a way to let language models update their own weights during a conversation, making them better at handling long contexts without retraining the entire model from scratch.

AI Agents Are Using Tools When They Should Just Think TL;DR — Multimodal AI agents call external tools way too often, even when they could answer questions just by looking at the image.

Your Multimodal Model Can See the Math Problem But Forgets How to Solve It TL;DR — Multimodal AI models with Mixture-of-Experts architecture can read text from images perfectly but somehow fail at reasoning tasks they'd solve easily if you just typed the…

This week we're diving deep into making AI systems more practical and understandable.

Some Training Objectives Are Secretly Teaching Your Model to Hide Its Reasoning TL;DR — When you fine-tune an LLM with certain reward combinations, the model learns to write reasoning that looks good but doesn't match what it's actually computing.

Your AI Agent's Secret Weakness Isn't the Model — It's the Harness TL;DR — The scaffolding around your AI agent (how it breaks down tasks, manages memory, and decides when to stop) matters more than you think, but it's usually buried in messy code that's…

Stop Paying for GPT-4 When GPT-3.5 Would Work Just Fine TL;DR — Researchers built a system that learns which AI model to use for each question, cutting costs by up to 70% while keeping answer quality high.

Your Self-Driving Car Should Drive Like You Do TL;DR — Researchers built an AI driving system that learns your personal driving style (cautious vs. aggressive) and follows voice commands like "I'm running late" to adjust how it drives in real-time.

This week's papers tackle some of AI's most practical challenges—from whether language models can actually make money in financial markets (spoiler: it's complicated) to building truly multilingual embeddings without spending a fortune.

Vision-Language Models Don't Need to Throw Away Pixels to Run Faster TL;DR — Instead of compressing images to speed up vision-language models, VISOR keeps all the pixels but makes the model look at them less often—getting 3-4x speedups while actually impr…

Your LLM Changes Its Mind Based on Irrelevant Context (And That's a Problem) TL;DR — LLMs give dramatically different answers to the exact same question depending on unrelated sentences that appear before it.

Hey everyone!

The Embedding Model That Actually Speaks Your Language TL;DR — A new family of embedding models covers 200+ languages (including underserved ones) in 8 different sizes, beats current leaders on 11 benchmarks, and releases everything openly so you can actu…